OpenRaiser/Intern-Atlas huggingface dataset:
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional functional safety approaches, which primarily verify the reliability, robustness, and adequacy of AV hardware and software systems from a vehicle-centric perspective, do not sufficiently address the AV's broader interactions and behavioral impact on the surrounding traffic environment. To overcome this limitation, we propose a paradigm shift toward behavioral safety, a comprehensive approach focused on evaluating AV responses and interactions within traffic environment. To systematically assess behavioral safety, we introduce a third-party AV safety assessment framework comprising two complementary evaluation components: Driver Licensing Test and Driving Intelligence Test. The Driver Licensing Test evaluates AV's reactive behaviors under controlled scenarios, ensuring basic behavioral competency. In contrast, the Driving Intelligence Test assesses AV's interactive behaviors within naturalistic traffic conditions, quantifying the frequency of safety-critical events to deliver statistically meaningful safety metrics before large-scale deployment. We validated our proposed framework using \texttt{Autoware.Universe}, an open-source Level 4 AV, tested both in simulated environments and on the physical test track at the University of Michigan's Mcity Testing Facility. The results indicate that \texttt{Autoware.Universe} passed 6 out of 14 scenarios and exhibited a crash rate of 3.01e-3 crashes per mile, approximately 1,000 times higher than average human driver crash rate. During the tests, we also uncovered several unknown unsafe scenarios for \texttt{Autoware.Universe}. These findings underscore the necessity of behavioral safety evaluations for improving AV safety performance prior to widespread public deployment.
Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning
Venue: arXiv cs.CL 2025
Year: 2025

Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary backgrounds. However, tailoring models to individual users typically requires substantial annotation efforts and computational resources due to user-specific finetuning. To address this, we present a systematic study of personalized jargon detection, focusing on methods that are both efficient and scalable for real-world deployment. We explore two personalization strategies: (1) lightweight finetuning using Low-Rank Adaptation (LoRA) on open-source models, and (2) personalized prompting, which tailors model behavior at inference time without retaining. To reflect realistic constraints, we also investigate semi-supervised approaches that combine limited annotated data with self-supervised learning from users' publications. Our personalized LoRA model outperforms GPT-4 with contextual prompting by 21.4% in F1 score and exceeds the best performing oracle baseline by 8.3%. Remarkably, our method achieves comparable performance using only 10% of the annotated training data, demonstrating its practicality for resource-constrained settings. Our study offers the first work to systematically explore efficient, low-resource personalization of jargon detection using open-source language models, offering a practical path toward scalable, user-adaptive NLP system.
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering
Venue: arXiv cs.CV 2025
Year: 2025

Computed Tomography (CT) scan, which produces 3D volumetric medical data that can be viewed as hundreds of cross-sectional images (a.k.a. slices), provides detailed anatomical information for diagnosis. For radiologists, creating CT radiology reports is time-consuming and error-prone. A visual question answering (VQA) system that can answer radiologists' questions about some anatomical regions on the CT scan and even automatically generate a radiology report is urgently needed. However, existing VQA systems cannot adequately handle the CT radiology question answering (CTQA) task for: (1) anatomic complexity makes CT images difficult to understand; (2) spatial relationship across hundreds slices is difficult to capture. To address these issues, this paper proposes CT-Agent, a multimodal agentic framework for CTQA. CT-Agent adopts anatomically independent tools to break down the anatomic complexity; furthermore, it efficiently captures the across-slice spatial relationship with a global-local token compression strategy. Experimental results on two 3D chest CT datasets, CT-RATE and RadGenome-ChestCT, verify the superior performance of CT-Agent.
Beyond Diagonal Intelligent Reflecting Surface Aided Integrated Sensing and Communication
Venue: arXiv cs.IT 2025
Year: 2025

Beyond diagonal intelligent reflecting surface (BD-IRS) is a new promising IRS architecture for which the reflection matrix is not limited to the diagonal structure as for conventional IRS. In this paper, we study a BD-IRS aided uplink integrated sensing and communication (ISAC) system where sensing is performed in a device-based manner. Specifically, we aim to estimate the unknown and random location of an active target based on its uplink probing signals sent to a multi-antenna base station (BS) as well as the known prior distribution information of the target's location. Multiple communication users also simultaneously send uplink signals, resulting in a challenging mutual interference issue between sensing and communication. We first characterize the sensing performance metric by deriving the posterior Cramér-Rao bound (PCRB) of the mean-squared error (MSE) when prior information is available. Then, we formulate a BD-IRS reflection matrix optimization problem to maximize the minimum expected achievable rate among the multiple users subject to a constraint on the PCRB as well as the lossless and reciprocal constraints on the BD-IRS reflection matrix. The formulated problem is non-convex and challenging to solve. To tackle this problem, we propose a penalty dual decomposition (PDD) based algorithm which can find a high-quality suboptimal solution with polynomial-time complexity. In addition, we propose and optimize a time-division multiple access (TDMA) based scheme which removes the sensing-communication mutual interference. Numerical results verify the effectiveness of the proposed designs and provide useful design insights such as the optimal choice of multiple access scheme.
Verifiable Exponential Mechanism for Median Estimation
Venue: arXiv cs.CR 2025
Year: 2025

Differential Privacy (DP) is a rigorous privacy standard widely adopted in data analysis and machine learning. However, its guarantees rely on correctly introducing randomized noise--an assumption that may not hold if the implementation is faulty or manipulated by an untrusted analyst. To address this concern, we propose the first verifiable implementation of the exponential mechanism using zk-SNARKs. As a concrete application, we present the first verifiable differentially private (DP) median estimation scheme, which leverages this construction to ensure both privacy and verifiability. Our method encodes the exponential mechanism and a utility function for the median into an arithmetic circuit, employing a scaled inverse CDF technique for sampling. This design enables cryptographic verification that the reported output adheres to the intended DP mechanism, ensuring both privacy and integrity without revealing sensitive data.
Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems
Venue: arXiv cs.LG 2025
Year: 2025

This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states.
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance
Venue: arXiv cs.AI 2025
Year: 2025

When using Large Language Models (LLMs) to support Knowledge Graph Engineering (KGE), one of the first indications when searching for an appropriate model is its size. According to the scaling laws, larger models typically show higher capabilities. However, in practice, resource costs are also an important factor and thus it makes sense to consider the ratio between model performance and costs. The LLM-KG-Bench framework enables the comparison of LLMs in the context of KGE tasks and assesses their capabilities of understanding and producing KGs and KG queries. Based on a dataset created in an LLM-KG-Bench run covering 26 open state-of-the-art LLMs, we explore the model size scaling laws specific to KGE tasks. In our analyses, we assess how benchmark scores evolve between different model size categories. Additionally, we inspect how the general score development of single models and families of models correlates to their size. Our analyses revealed that, with a few exceptions, the model size scaling laws generally also apply to the selected KGE tasks. However, in some cases, plateau or ceiling effects occurred, i.e., the task performance did not change much between a model and the next larger model. In these cases, smaller models could be considered to achieve high cost-effectiveness. Regarding models of the same family, sometimes larger models performed worse than smaller models of the same family. These effects occurred only locally. Hence it is advisable to additionally test the next smallest and largest model of the same family.
ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay
Venue: arXiv cs.CV 2025
Year: 2025

Training large language models (LLMs) as interactive agents for controlling graphical user interfaces (GUIs) presents a unique challenge to optimize long-horizon action sequences with multimodal feedback from complex environments. While recent works have advanced multi-turn reinforcement learning (RL) for reasoning and tool-using capabilities in LLMs, their application to GUI-based agents remains relatively underexplored due to the difficulty of sparse rewards, delayed feedback, and high rollout costs. In this paper, we investigate end-to-end policy optimization for vision-language-based GUI agents with the aim of improving performance on complex, long-horizon computer tasks. We propose Agentic Replay Policy Optimization (ARPO), an end-to-end RL approach that augments Group Relative Policy Optimization (GRPO) with a replay buffer to reuse the successful experience across training iterations. To further stabilize the training process, we propose a task selection strategy that filters tasks based on baseline agent performance, allowing the agent to focus on learning from informative interactions. Additionally, we compare ARPO with offline preference optimization approaches, highlighting the advantages of policy-based methods in GUI environments. Experiments on the OSWorld benchmark demonstrate that ARPO achieves competitive results, establishing a new performance baseline for LLM-based GUI agents trained via reinforcement learning. Our findings underscore the effectiveness of reinforcement learning for training multi-turn, vision-language GUI agents capable of managing complex real-world UI interactions. Codes and models:https://github.com/dvlab-research/ARPO.git.
Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models
Venue: arXiv cs.SD 2025
Year: 2025

Recently, pre-trained models for music information retrieval based on self-supervised learning (SSL) are becoming popular, showing success in various downstream tasks. However, there is limited research on the specific meanings of the encoded information and their applicability. Exploring these aspects can help us better understand their capabilities and limitations, leading to more effective use in downstream tasks. In this study, we analyze the advanced music representation model MusicFM and the newly emerged SSL model MuQ. We focus on three main aspects: (i) validating the advantages of SSL models across multiple downstream tasks, (ii) exploring the specialization of layer-wise information for different tasks, and (iii) comparing performance differences when selecting specific layers. Through this analysis, we reveal insights into the structure and potential applications of SSL models in music information retrieval.
Improving Chemical Understanding of LLMs via SMILES Parsing
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) are increasingly recognized as powerful tools for scientific discovery, particularly in molecular science. A fundamental requirement for these models is the ability to accurately understand molecular structures, commonly encoded in the SMILES representation. However, current LLMs struggle to interpret SMILES, even failing to carry out basic tasks such as counting molecular rings. To address this limitation, we introduce CLEANMOL, a novel framework that formulates SMILES parsing into a suite of clean and deterministic tasks explicitly designed to promote graph-level molecular comprehension. These tasks span from subgraph matching to global graph matching, providing structured supervision aligned with molecular structural properties. We construct a molecular pretraining dataset with adaptive difficulty scoring and pre-train open-source LLMs on these tasks. Our results show that CLEANMOL not only enhances structural comprehension but also achieves the best or competes with the baseline on the Mol-Instructions benchmark.
Estimating Perceptual Attributes of Haptic Textures Using Visuo-Tactile Data
Venue: arXiv cs.HC 2025
Year: 2025

Accurate prediction of perceptual attributes of haptic textures is essential for advancing VR and AR applications and enhancing robotic interaction with physical surfaces. This paper presents a deep learning-based multi-modal framework, incorporating visual and tactile data, to predict perceptual texture ratings by leveraging multi-feature inputs. To achieve this, a four-dimensional haptic attribute space encompassing rough-smooth, flat-bumpy, sticky-slippery, and hard-soft dimensions is first constructed through psychophysical experiments, where participants evaluate 50 diverse real-world texture samples. A physical signal space is subsequently created by collecting visual and tactile data from these textures. Finally, a deep learning architecture integrating a CNN-based autoencoder for visual feature learning and a ConvLSTM network for tactile data processing is trained to predict user-assigned attribute ratings. This multi-modal, multi-feature approach maps physical signals to perceptual ratings, enabling accurate predictions for unseen textures. To evaluate predictive accuracy, we employed leave-one-out cross-validation to rigorously assess the model's reliability and generalizability against several machine learning and deep learning baselines. Experimental results demonstrate that the framework consistently outperforms single-modality approaches, achieving lower MAE and RMSE, highlighting the efficacy of combining visual and tactile modalities.
Arrival Control in Quasi-Reversible Queueing Systems: Optimization and Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we introduce a versatile scheme for optimizing the arrival rates of quasi-reversible queueing systems. We first propose an alternative definition of quasi-reversibility that encompasses reversibility and highlights the importance of the definition of customer classes. In a second time, we introduce balanced arrival control policies, which generalize the notion of balanced arrival rates introduced in the context of Whittle networks, to the much broader class of quasi-reversible queueing systems. We prove that supplementing a quasi-reversible queueing system with a balanced arrival-control policy preserves the quasi-reversibility, and we specify the form of the stationary measures. We revisit two canonical examples of quasi-reversible queueing systems, Whittle networks and order-independent queues. Lastly, we focus on the problem of admission control and leverage our results in the frameworks of optimization and reinforcement learning.
AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training
Venue: arXiv cs.LG 2025
Year: 2025

We introduce AdamS, a simple yet effective alternative to Adam for large language model (LLM) pretraining and post-training. By leveraging a novel denominator, i.e., the root of weighted sum of squares of the momentum and the current gradient, AdamS eliminates the need for second-moment estimates. Hence, AdamS is efficient, matching the memory and compute footprint of SGD with momentum while delivering superior optimization performance. Moreover, AdamS is easy to adopt: it can directly inherit hyperparameters of AdamW, and is entirely model-agnostic, integrating seamlessly into existing pipelines without modifications to optimizer APIs or architectures. The motivation behind AdamS stems from the observed $(L_0, L_1)$ smoothness properties in transformer objectives, where local smoothness is governed by gradient magnitudes that can be further approximated by momentum magnitudes. We establish rigorous theoretical convergence guarantees and provide practical guidelines for hyperparameter selection. Empirically, AdamS demonstrates strong performance in various tasks, including pre-training runs on GPT-2 and Llama2 (up to 13B parameters) and reinforcement learning in post-training regimes. With its efficiency, simplicity, and theoretical grounding, AdamS stands as a compelling alternative to existing optimizers.
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems
Venue: arXiv cs.IR 2025
Year: 2025

Retrieval-augmented generation (RAG) systems can effectively mitigate the hallucination problem of large language models (LLMs),but they also possess inherent vulnerabilities. Identifying these weaknesses before the large-scale real-world deployment of RAG systems is of great importance, as it lays the foundation for building more secure and robust RAG systems in the future. Existing adversarial attack methods typically exploit knowledge base poisoning to probe the vulnerabilities of RAG systems, which can effectively deceive standard RAG models. However, with the rapid advancement of deep reasoning capabilities in modern LLMs, previous approaches that merely inject incorrect knowledge are inadequate when attacking RAG systems equipped with deep reasoning abilities. Inspired by the deep thinking capabilities of LLMs, this paper extracts reasoning process templates from R1-based RAG systems, uses these templates to wrap erroneous knowledge into adversarial documents, and injects them into the knowledge base to attack RAG systems. The key idea of our approach is that adversarial documents, by simulating the chain-of-thought patterns aligned with the model's training signals, may be misinterpreted by the model as authentic historical reasoning processes, thus increasing their likelihood of being referenced. Experiments conducted on the MS MARCO passage ranking dataset demonstrate the effectiveness of our proposed method.
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving
Venue: arXiv cs.RO 2025
Year: 2025

Reinforcement learning (RL)-based autonomous driving policy learning faces critical limitations such as low sample efficiency and poor generalization; its reliance on online interactions and trial-and-error learning is especially unacceptable in safety-critical scenarios. Existing methods including safe RL often fail to capture the true semantic meaning of "safety" in complex driving contexts, leading to either overly conservative driving behavior or constraint violations. To address these challenges, we propose VL-SAFE, a world model-based safe RL framework with Vision-Language model (VLM)-as-safety-guidance paradigm, designed for offline safe policy learning. Specifically, we construct offline datasets containing data collected by expert agents and labeled with safety scores derived from VLMs. A world model is trained to generate imagined rollouts together with safety estimations, allowing the agent to perform safe planning without interacting with the real environment. Based on these imagined trajectories and safety evaluations, actor-critic learning is conducted under VLM-based safety guidance to optimize the driving policy more safely and efficiently. Extensive evaluations demonstrate that VL-SAFE achieves superior sample efficiency, generalization, safety, and overall performance compared to existing baselines. To the best of our knowledge, this is the first work that introduces a VLM-guided world model-based approach for safe autonomous driving. The demo video and code can be accessed at: https://ys-qu.github.io/vlsafe-website/
Serious Games: Human-AI Interaction, Evolution, and Coevolution
Venue: arXiv cs.AI 2025
Year: 2025

The serious games between humans and AI have only just begun. Evolutionary Game Theory (EGT) models the competitive and cooperative strategies of biological entities. EGT could help predict the potential evolutionary equilibrium of humans and AI. The objective of this work was to examine some of the EGT models relevant to human-AI interaction, evolution, and coevolution. Of thirteen EGT models considered, three were examined: the Hawk-Dove Game, Iterated Prisoner's Dilemma, and the War of Attrition. This selection was based on the widespread acceptance and clear relevance of these models to potential human-AI evolutionary dynamics and coevolutionary trajectories. The Hawk-Dove Game predicts balanced mixed-strategy equilibria based on the costs of conflict. It also shows the potential for balanced coevolution rather than dominance. Iterated Prisoner's Dilemma suggests that repeated interaction may lead to cognitive coevolution. It demonstrates how memory and reciprocity can lead to cooperation. The War of Attrition suggests that competition for resources may result in strategic coevolution, asymmetric equilibria, and conventions on sharing resources. Therefore, EGT may provide a suitable framework to understand and predict the human-AI evolutionary dynamic. However, future research could extend beyond EGT and explore additional frameworks, empirical validation methods, and interdisciplinary perspectives. AI is being shaped by human input and is evolving in response to it. So too, neuroplasticity allows the human brain to grow and evolve in response to stimuli. If humans and AI converge in future, what might be the result of human neuroplasticity combined with an ever-evolving AI? Future research should be mindful of the ethical and cognitive implications of human-AI interaction, evolution, and coevolution.